gmourin_-_fisrts-test-sql-gguf – AI Model by RichardErkhov | AlphaNeural AI

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gmourin_-_fisrts-test-sql-gguf

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Quantization made by Richard Erkhov.

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fisrts-test-sql - GGUF

Model creator: https://huggingface.co/gmourin/
Original model: https://huggingface.co/gmourin/fisrts-test-sql/

Name	Quant method	Size
fisrts-test-sql.Q2_K.gguf	Q2_K	0.06GB
fisrts-test-sql.IQ3_XS.gguf	IQ3_XS	0.07GB
fisrts-test-sql.IQ3_S.gguf	IQ3_S	0.07GB
fisrts-test-sql.Q3_K_S.gguf	Q3_K_S	0.07GB
fisrts-test-sql.IQ3_M.gguf	IQ3_M	0.07GB
fisrts-test-sql.Q3_K.gguf	Q3_K	0.07GB
fisrts-test-sql.Q3_K_M.gguf	Q3_K_M	0.07GB
fisrts-test-sql.Q3_K_L.gguf	Q3_K_L	0.07GB
fisrts-test-sql.IQ4_XS.gguf	IQ4_XS	0.07GB
fisrts-test-sql.Q4_0.gguf	Q4_0	0.08GB
fisrts-test-sql.IQ4_NL.gguf	IQ4_NL	0.08GB
fisrts-test-sql.Q4_K_S.gguf	Q4_K_S	0.08GB
fisrts-test-sql.Q4_K.gguf	Q4_K	0.08GB
fisrts-test-sql.Q4_K_M.gguf	Q4_K_M	0.08GB
fisrts-test-sql.Q4_1.gguf	Q4_1	0.08GB
fisrts-test-sql.Q5_0.gguf	Q5_0	0.09GB
fisrts-test-sql.Q5_K_S.gguf	Q5_K_S	0.09GB
fisrts-test-sql.Q5_K.gguf	Q5_K	0.09GB
fisrts-test-sql.Q5_K_M.gguf	Q5_K_M	0.09GB
fisrts-test-sql.Q5_1.gguf	Q5_1	0.09GB
fisrts-test-sql.Q6_K.gguf	Q6_K	0.1GB
fisrts-test-sql.Q8_0.gguf	Q8_0	0.12GB

Original model description:

library_name: transformers license: apache-2.0 base_model: distilgpt2 tags:

generated_from_trainer model-index:
name: fisrts-test-sql results: []

fisrts-test-sql

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.9426

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	1	3.0238
No log	2.0	2	2.9607
No log	3.0	3	2.9426

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0